1. Field of the Invention
This invention relates generally to an industrial process, and, more particularly, to various methods and systems for prioritizing material to clear exception conditions.
2. Description of the Related Art
After a complete reading of the present application, those skilled in the relevant art will understand that the present invention has broad application to a variety of industries involving the manufacture of a variety of different types of devices or workpieces. By way of example only, the background of the application will be discussed in the context of various problems encountered in the manufacture of integrated circuit devices. However, the present invention is not to be considered as limited to use only within the semiconductor manufacturing industry.
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing the defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
Generally, a set of processing steps is performed on a lot of wafers using a variety of process tools, including photolithography steppers, etch tools, deposition tools, polishing tools, thermal anneal process tools, implantation tools, etc. The technologies underlying semiconductor process tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the process tools that are currently commercially available suffer certain deficiencies. In particular, some of such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender non-optimal control of critical processing parameters, such as throughput, accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
One technique for improving the operation of a semiconductor processing line includes using a factory wide control system to automatically control the operation of the various process tools. The manufacturing tools communicate with a manufacturing frame-work or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface that facilitates communications between the manufacturing tool and the manufacturing frame-work. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script based upon a manufacturing model, which can be a software program that automatically retrieves the data needed to execute a manufacturing process. Often, semiconductor devices are staged through multiple manufacturing tools for multiple processes, generating data relating to the quality of the processed semiconductor devices.
During the fabrication process various events may take place that affect the perform-ance of the devices being fabricated. That is, variations in the fabrication process steps result in device performance variations. Factors, such as feature critical dimensions, doping levels, contact resistance, particle contamination, etc., all may potentially affect the end performance of the device. Various tools in the processing line are controlled in accordance with perform-ance models to reduce processing variation. Commonly controlled tools include photo-lithography steppers, polishing tools, etching tools, and deposition tools. Pre-processing and/or post-processing metrology data is supplied to process controllers for the tools. Operating recipe parameters, such as processing time, are calculated by the process controllers based on the performance model and the metrology information to attempt to achieve post-processing results as close to a target value as possible. Reducing variation in this manner leads to increased throughput, reduced cost, higher device performance, etc., all of which equate to increased profitability.
In current day manufacturing environments, a variety of automated process control applications may be employed to control manufacturing activities. However, when automated process control is implemented, it is not uncommon to also implement various business rules to limit the risk associated with the implementation of the advanced process control applications. For example, a business rule may be adopted to require special processing to initialize a controller, i.e., an initialization exception condition, or to limit the amount of product that can be processed without obtaining metrology feedback, i.e., a jeopardy exception condition.
In general, an exception condition may be understood to be a condition that would interfere with running production wafers in the fabrication facility. For example, an exception condition may be considered to arise when a workpiece has an associated quantity of material, i.e., additional workpieces, that cannot be processed until the initial workpiece has been successfully processed. There may be many different types of exception conditions within the manufacturing facility at any given time. For example, a new process recipe may be implemented with a particular operation. However, since the process recipe is new, there is no historical metrology data associated with devices produced using the new recipe. Thus, in one example, a business rule may be adopted such that an initial lot of wafers may be processed using the new recipe, but additional lots of wafers cannot be processed until metrology data is acquired and analyzed for this initial lot. In short, this manufacturing operation may, in effect, be on hold pending the acquisition and analysis of metrology data related to this initial lot. Each of the various exception conditions have the potential to disrupt manufacturing activities by preventing material from being processed. In general, normal production activities cannot continue until certain material can clear the exception condition.
For manufacturing operations to proceed effectively, methods have been developed for prioritizing the processing of material to clear such exception conditions. In some cases, specific priorities are assigned to different exception types, e.g., initializing, jeopardy, etc. For example, processing materials, e.g., a lot of wafers, that clear an initialization exception is given a higher priority than a lot that can clear a jeopardy exception. However, such a methodology of clearing exceptions by exception type may not be the most efficient in terms of overall operation of the manufacturing facility.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.